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spatialPooler_test.go
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spatialPooler_test.go
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package htm
import (
//"fmt"
"github.com/nupic-community/htm/utils"
"github.com/skelterjohn/go.matrix"
//"github.com/stretchr/testify/assert"
"github.com/zacg/testify/assert"
//"math/big"
//"github.com/stretchr/testify/mock"
"math"
"math/rand"
"strconv"
"testing"
)
func getConnected(perm []float64, sp *SpatialPooler) (int, []bool) {
numcon := 0
connected := make([]bool, len(perm))
for i := 0; i < len(perm); i++ {
if perm[i] >= sp.SynPermConnected {
numcon++
connected[i] = true
} else {
connected[i] = false
}
}
return numcon, connected
}
func TestPermanenceInit(t *testing.T) {
sp := SpatialPooler{}
sp.InputDimensions = []int{10}
sp.numInputs = 10
sp.SynPermConnected = 0.1
sp.SynPermActiveInc = 0.1
sp.PotentialRadius = 2
connectedPct := 1.0
mask := []bool{true, true, true, false, false, false, false, false, true, true}
perm := sp.initPermanence(mask, connectedPct)
numcon, connected := getConnected(perm, &sp)
if numcon != 5 {
t.Errorf("numcon was %v expected 5", numcon)
}
maxThresh := sp.SynPermConnected + sp.SynPermActiveInc/4
for i := 0; i < len(perm); i++ {
if perm[i] > maxThresh {
t.Errorf("perm %v was %v higher than threshold", i, perm[i])
}
}
connectedPct = 0
numcon = 0
perm = sp.initPermanence(mask, connectedPct)
numcon, connected = getConnected(perm, &sp)
if numcon != 0 {
t.Errorf("numcon was %v expected false", numcon)
}
if len(connected) != 5 {
return
}
connectedPct = 0.5
sp.PotentialRadius = 100
sp.numInputs = 100
mask = make([]bool, 100)
for i := 0; i < len(mask); i++ {
mask[i] = true
}
perm = sp.initPermanence(mask, connectedPct)
numcon, connected = getConnected(perm, &sp)
if !(numcon > 0) {
t.Errorf("numcon was %v expected greater than false", numcon)
}
if numcon >= sp.numInputs {
t.Errorf("numcon was %v expected less than inputs count", numcon)
}
minThresh := sp.SynPermActiveInc / 2.0
connThresh := sp.SynPermConnected
for i := 0; i < len(perm); i++ {
if perm[i] < minThresh {
t.Errorf("perm %v was %v less than min threshold", i, perm[i])
}
if perm[i] >= connThresh {
t.Errorf("perm %v was %v not less than connection threshold", i, perm[i])
}
}
}
func TestRaisePermanenceThreshold(t *testing.T) {
sp := SpatialPooler{}
sp.InputDimensions = []int{5}
sp.ColumnDimensions = []int{5}
sp.numColumns = 5
sp.numInputs = 5
sp.SynPermConnected = 0.1
sp.StimulusThreshold = 3
sp.SynPermBelowStimulusInc = 0.01
sp.SynPermMin = 0
sp.SynPermMax = 1
elms := make(map[int]float64, 25)
sp.permanences = matrix.MakeSparseMatrix(elms, 5, 5)
p := [][]float64{
{0.0, 0.11, 0.095, 0.092, 0.01},
{0.12, 0.15, 0.02, 0.12, 0.09},
{0.51, 0.081, 0.025, 0.089, 0.31},
{0.18, 0.0601, 0.11, 0.011, 0.03},
{0.011, 0.011, 0.011, 0.011, 0.011},
}
AddDenseToSparseHelper(p, sp.permanences)
sp.connectedSynapses = NewDenseBinaryMatrixFromDense([][]bool{
{false, true, false, false, false},
{true, true, false, true, false},
{true, false, false, false, true},
{true, false, true, false, false},
{false, false, false, false, false},
})
sp.connectedCounts = []int{1, 3, 2, 2, 0}
truePermanences := [][]float64{
{0.01, 0.12, 0.105, 0.102, 0.02},
{0.12, 0.15, 0.02, 0.12, 0.09},
{0.53, 0.101, 0.045, 0.109, 0.33},
{0.22, 0.1001, 0.15, 0.051, 0.07},
{0.101, 0.101, 0.101, 0.101, 0.101},
}
maskPP := []int{0, 1, 2, 3, 4}
for i := 0; i < sp.numColumns; i++ {
perm := SparseMatrixToArray(sp.permanences.GetRowVector(i))
sp.raisePermanenceToThreshold(perm, maskPP)
for j := 0; j < sp.numInputs; j++ {
//if truePermanences[i][j] != perm[j] {
if !AlmostEqualFloat(truePermanences[i][j], perm[j]) {
t.Errorf("truePermances: %v != perm: %v", truePermanences[i][j], perm[j])
}
}
}
}
func TestStripNever(t *testing.T) {
sp := SpatialPooler{}
sp.activeDutyCycles = []float64{0.50, 0.1, 0, 0.2, 0.4, 0}
activeColumns := []int{0, 1, 2, 4}
stripped := sp.stripNeverLearned(activeColumns)
trueStripped := []int{0, 1, 4}
t.Logf("stripped %v", stripped)
for i := 0; i < len(trueStripped); i++ {
if stripped[i] != trueStripped[i] {
t.Errorf("stripped %v was %v expected %v", i, stripped[i], trueStripped[i])
}
}
sp.activeDutyCycles = []float64{0.9, 0, 0, 0, 0.4, 0.3}
activeColumns = []int{0, 1, 2, 3, 4, 5}
stripped = sp.stripNeverLearned(activeColumns)
trueStripped = []int{0, 4, 5}
for i := 0; i < len(trueStripped); i++ {
if stripped[i] != trueStripped[i] {
t.Errorf("stripped %v was %v expected %v", i, stripped[i], trueStripped[i])
}
}
sp.activeDutyCycles = []float64{0, 0, 0, 0, 0, 0}
activeColumns = []int{0, 1, 2, 3, 4, 5}
stripped = sp.stripNeverLearned(activeColumns)
if len(stripped) != 0 {
t.Errorf("Expected empty stripped was %v", stripped)
}
sp.activeDutyCycles = []float64{1, 1, 1, 1, 1, 1}
activeColumns = []int{0, 1, 2, 3, 4, 5}
stripped = sp.stripNeverLearned(activeColumns)
trueStripped = []int{0, 1, 2, 3, 4, 5}
for i := 0; i < len(trueStripped); i++ {
if stripped[i] != trueStripped[i] {
t.Errorf("stripped %v was %v expected %v", i, stripped[i], trueStripped[i])
}
}
}
func TestAvgConnectedSpanForColumn2D(t *testing.T) {
sp := SpatialPooler{}
sp.InputDimensions = []int{8}
sp.numInputs = 8
sp.numColumns = 9
sp.ColumnDimensions = []int{9}
ints := [][]int{{0, 1, 0, 1, 0, 1, 0, 1},
{0, 0, 0, 1, 0, 0, 0, 1},
{0, 0, 0, 0, 0, 0, 1, 0},
{0, 0, 1, 0, 0, 0, 1, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 1, 1, 0, 0, 0, 0, 0},
{0, 0, 1, 1, 1, 0, 0, 0},
{0, 0, 1, 0, 1, 0, 0, 0},
{1, 1, 1, 1, 1, 1, 1, 1}}
sp.connectedSynapses = NewDenseBinaryMatrixFromInts(ints)
trueAvgConnectedSpan := []int{7, 5, 1, 5, 0, 2, 3, 3, 8}
for i := 0; i < sp.numColumns; i++ {
connectedSpan := sp.avgConnectedSpanForColumnND(i)
assert.Equal(t, trueAvgConnectedSpan[i], connectedSpan)
}
}
func TestAvgConnectedSpanForColumnND(t *testing.T) {
sp := SpatialPooler{}
sp.InputDimensions = []int{4, 4, 2, 5}
sp.numInputs = utils.ProdInt(sp.InputDimensions)
sp.numColumns = 5
sp.ColumnDimensions = []int{0, 1, 2, 3, 4}
sp.connectedSynapses = NewDenseBinaryMatrix(sp.numColumns, sp.numInputs)
connected := make([]bool, sp.numInputs)
connected[(1*40)+(0*10)+(1*5)+(0*1)] = true
connected[(1*40)+(0*10)+(1*5)+(1*1)] = true
connected[(3*40)+(2*10)+(1*5)+(0*1)] = true
connected[(3*40)+(0*10)+(1*5)+(0*1)] = true
connected[(1*40)+(0*10)+(1*5)+(3*1)] = true
connected[(2*40)+(2*10)+(1*5)+(0*1)] = true
//# span: 3 3 1 4, avg = 11/4
sp.connectedSynapses.ReplaceRow(0, connected)
connected2 := make([]bool, sp.numInputs)
connected2[(2*40)+(0*10)+(1*5)+(0*1)] = true
connected2[(2*40)+(0*10)+(0*5)+(0*1)] = true
connected2[(3*40)+(0*10)+(0*5)+(0*1)] = true
connected2[(3*40)+(0*10)+(1*5)+(0*1)] = true
//spn: 2 1 2 1, avg = 6/4
sp.connectedSynapses.ReplaceRow(1, connected2)
connected3 := make([]bool, sp.numInputs)
connected3[(0*40)+(0*10)+(1*5)+(4*1)] = true
connected3[(0*40)+(0*10)+(0*5)+(3*1)] = true
connected3[(0*40)+(0*10)+(0*5)+(1*1)] = true
connected3[(1*40)+(0*10)+(0*5)+(2*1)] = true
connected3[(0*40)+(0*10)+(1*5)+(1*1)] = true
connected3[(3*40)+(3*10)+(1*5)+(1*1)] = true
// span: 4 4 2 4, avg = 14/4
sp.connectedSynapses.ReplaceRow(2, connected3)
connected4 := make([]bool, sp.numInputs)
connected4[(3*40)+(3*10)+(1*5)+(4*1)] = true
connected4[(0*40)+(0*10)+(0*5)+(0*1)] = true
// span: 4 4 2 5, avg = 15/4
sp.connectedSynapses.ReplaceRow(3, connected4)
connected5 := make([]bool, sp.numInputs)
//# span: false false false false, avg = false
sp.connectedSynapses.ReplaceRow(4, connected5)
//t.Logf("width: %v", sp.connectedSynapses.Width)
trueAvgConnectedSpan := []float64{11.0 / 4.0, 6.0 / 4.0, 14.0 / 4.0, 15.0 / 4.0, 0.0}
for i, tspan := range trueAvgConnectedSpan {
connectedSpan := sp.avgConnectedSpanForColumnND(i)
if connectedSpan != tspan {
t.Errorf("Connected span was: %v expected: %v", connectedSpan, tspan)
}
}
}
func TestAvgColumnsPerInput(t *testing.T) {
sp := SpatialPooler{}
sp.ColumnDimensions = []int{2, 2, 2, 2}
sp.InputDimensions = []int{4, 4, 4, 4}
if sp.avgColumnsPerInput() != 0.5 {
t.Errorf("Expected %v avg columns, was: %v", 0.5, sp.avgColumnsPerInput())
}
sp.ColumnDimensions = []int{2, 2, 2, 2}
sp.InputDimensions = []int{7, 5, 1, 3}
//2/7 false.4 2 false.666
trueAvgColumnPerInput := (2.0/7 + 2.0/5 + 2.0/1 + 2/3.0) / 4
if sp.avgColumnsPerInput() != trueAvgColumnPerInput {
t.Errorf("Expected %v avg columns, was: %v", trueAvgColumnPerInput, sp.avgColumnsPerInput())
}
sp.ColumnDimensions = []int{3, 3}
sp.InputDimensions = []int{3, 3}
// 1 1
trueAvgColumnPerInput = 1
if sp.avgColumnsPerInput() != trueAvgColumnPerInput {
t.Errorf("Expected %v avg columns, was: %v", trueAvgColumnPerInput, sp.avgColumnsPerInput())
}
sp.ColumnDimensions = []int{25}
sp.InputDimensions = []int{5}
// 5
trueAvgColumnPerInput = 5
if sp.avgColumnsPerInput() != trueAvgColumnPerInput {
t.Errorf("Expected %v avg columns, was: %v", trueAvgColumnPerInput, sp.avgColumnsPerInput())
}
sp.ColumnDimensions = []int{3, 3, 3, 5, 5, 6, 6}
sp.InputDimensions = []int{3, 3, 3, 5, 5, 6, 6}
//1 1 1 1 1 1 1
trueAvgColumnPerInput = 1
if sp.avgColumnsPerInput() != trueAvgColumnPerInput {
t.Errorf("Expected %v avg columns, was: %v", trueAvgColumnPerInput, sp.avgColumnsPerInput())
}
sp.ColumnDimensions = []int{3, 6, 9, 12}
sp.InputDimensions = []int{3, 3, 3, 3}
// 1 2 3 4
trueAvgColumnPerInput = 2.5
if sp.avgColumnsPerInput() != trueAvgColumnPerInput {
t.Errorf("Expected %v avg columns, was: %v", trueAvgColumnPerInput, sp.avgColumnsPerInput())
}
}
func TestUpdateInhibitionRadius(t *testing.T) {
sp := SpatialPooler{}
// Test global inhibition case
sp.GlobalInhibition = true
sp.ColumnDimensions = []int{57, 31, 2}
sp.numColumns = 3
sp.updateInhibitionRadius(sp.avgConnectedSpanForColumnND, sp.avgColumnsPerInput)
expected := 57
assert.Equal(t, expected, sp.inhibitionRadius)
sp.GlobalInhibition = false
avgConnectedSpan := 3.0
avgColPerInput := 4.0
var avgConnectedSpanMock = func(i int) float64 {
return avgConnectedSpan
}
var avgColPerInputMock = func() float64 {
return avgColPerInput
}
trueInhibitionRadius := 6
//((3 * 4) - 1) / 2 => round up
sp.updateInhibitionRadius(avgConnectedSpanMock, avgColPerInputMock)
assert.Equal(t, trueInhibitionRadius, sp.inhibitionRadius)
//Test clipping at 1.false
sp.GlobalInhibition = false
trueInhibitionRadius = 1
avgConnectedSpan = 0.5
avgColPerInput = 1.2
sp.updateInhibitionRadius(avgConnectedSpanMock, avgColPerInputMock)
assert.Equal(t, trueInhibitionRadius, sp.inhibitionRadius)
// //Test rounding up
sp.GlobalInhibition = false
avgConnectedSpan = 2.4
avgColPerInput = 2
trueInhibitionRadius = 2
// ((2 * 2.4) - 1) / 2.false => round up
sp.updateInhibitionRadius(avgConnectedSpanMock, avgColPerInputMock)
assert.Equal(t, trueInhibitionRadius, sp.inhibitionRadius)
}
func TestCalculateOverlap(t *testing.T) {
sp := SpatialPooler{}
sp.numInputs = 10
sp.numColumns = 5
sp.InputDimensions = []int{10}
sp.ColumnDimensions = []int{5}
sp.connectedSynapses = NewDenseBinaryMatrixFromDense([][]bool{
{true, true, true, true, true, true, true, true, true, true},
{false, false, true, true, true, true, true, true, true, true},
{false, false, false, false, true, true, true, true, true, true},
{false, false, false, false, false, false, true, true, true, true},
{false, false, false, false, false, false, false, false, true, true},
})
t.Log(sp.connectedSynapses.ToString())
sp.connectedCounts = []int{10, 8, 6, 4, 2}
inputVector := make([]bool, sp.numInputs)
overlaps := sp.calculateOverlap(inputVector)
overlapsPct := sp.calculateOverlapPct(overlaps)
trueOverlaps := []int{0, 0, 0, 0, 0}
trueOverlapsPct := []float64{0, 0, 0, 0, 0}
t.Logf("pct %v", overlapsPct)
assert.Equal(t, trueOverlaps, overlaps)
assert.Equal(t, overlapsPct, trueOverlapsPct)
inputVector = make([]bool, sp.numInputs)
for i := 0; i < len(inputVector); i++ {
inputVector[i] = true
}
overlaps = sp.calculateOverlap(inputVector)
overlapsPct = sp.calculateOverlapPct(overlaps)
trueOverlaps = []int{10, 8, 6, 4, 2}
trueOverlapsPct = []float64{1, 1, 1, 1, 1}
t.Logf("pct %v", overlapsPct)
assert.Equal(t, trueOverlaps, overlaps)
assert.Equal(t, overlapsPct, trueOverlapsPct)
inputVector = make([]bool, sp.numInputs)
inputVector[9] = true
t.Logf("input %v", inputVector)
overlaps = sp.calculateOverlap(inputVector)
overlapsPct = sp.calculateOverlapPct(overlaps)
trueOverlaps = []int{1, 1, 1, 1, 1}
trueOverlapsPct = []float64{0.1, 0.125, 1.0 / 6, 0.25, 0.5}
t.Logf("pct %v", overlapsPct)
assert.Equal(t, trueOverlaps, overlaps)
assert.Equal(t, trueOverlapsPct, overlapsPct)
//Zig-zag
sp.connectedSynapses = NewDenseBinaryMatrixFromDense([][]bool{
{true, false, false, false, false, true, false, false, false, false},
{false, true, false, false, false, false, true, false, false, false},
{false, false, true, false, false, false, false, true, false, false},
{false, false, false, true, false, false, false, false, true, false},
{false, false, false, false, true, false, false, false, false, true},
})
sp.connectedCounts = []int{2.0, 2.0, 2.0, 2.0, 2.0}
inputVector = make([]bool, sp.numInputs)
inputVector[0] = true
inputVector[2] = true
inputVector[4] = true
inputVector[6] = true
inputVector[8] = true
t.Logf("input %v", inputVector)
overlaps = sp.calculateOverlap(inputVector)
overlapsPct = sp.calculateOverlapPct(overlaps)
trueOverlaps = []int{1, 1, 1, 1, 1}
trueOverlapsPct = []float64{0.5, 0.5, 0.5, 0.5, 0.5}
t.Logf("pct %v", overlapsPct)
assert.Equal(t, trueOverlaps, overlaps)
assert.Equal(t, trueOverlapsPct, overlapsPct)
}
func TestInhibitColumns(t *testing.T) {
sp := SpatialPooler{}
globalValue := []int{1}
localValue := []int{2}
lastGlobalDensity := 0.0
lastLocalDensity := 0.0
globalFunc := func(overlaps []float64, density float64) []int {
lastGlobalDensity = density
return globalValue
}
localFunc := func(overlaps []float64, density float64) []int {
lastLocalDensity = density
return localValue
}
sp.ColumnDimensions = []int{5}
sp.numColumns = 5
sp.inhibitionRadius = 10
sp.tieBreaker = make([]float64, sp.numColumns)
for i := 0; i < len(sp.tieBreaker); i++ {
sp.tieBreaker[i] = 0.01 * rand.Float64()
}
overlaps := utils.RandomSample(sp.numColumns)
sp.NumActiveColumnsPerInhArea = 5
sp.LocalAreaDensity = 0.1
sp.GlobalInhibition = true
sp.inhibitionRadius = 5
trueDensity := sp.LocalAreaDensity
sp.InhibitColumns(overlaps, globalFunc, localFunc)
assert.Equal(t, trueDensity, lastGlobalDensity)
//----- 2
sp.ColumnDimensions = []int{50, 10}
sp.numColumns = 500
sp.tieBreaker = utils.MakeSliceFloat64(500, 0)
sp.NumActiveColumnsPerInhArea = -1
sp.LocalAreaDensity = 0.1
sp.GlobalInhibition = false
sp.inhibitionRadius = 7
// 0.1 * (2*9+1)**2 = 22.5
trueDensity = sp.LocalAreaDensity
overlaps = utils.RandomSample(sp.numColumns)
sp.InhibitColumns(overlaps, globalFunc, localFunc)
assert.Equal(t, trueDensity, lastLocalDensity)
// Test translation of numColumnsPerInhArea into local area density
sp.ColumnDimensions = []int{10, 10}
sp.numColumns = 1000
sp.tieBreaker = utils.MakeSliceFloat64(1000, 0)
sp.NumActiveColumnsPerInhArea = 3
sp.LocalAreaDensity = -1
sp.GlobalInhibition = false
sp.inhibitionRadius = 4
trueDensity = 3.0 / 81.0
overlaps = utils.RandomSample(sp.numColumns)
// 3.0 / (((2*4) + 1) ** 2)
sp.InhibitColumns(overlaps, globalFunc, localFunc)
assert.Equal(t, trueDensity, lastLocalDensity)
// Test clipping of local area density to 0.5
sp.ColumnDimensions = []int{10, 10}
sp.numColumns = 1000
sp.tieBreaker = utils.MakeSliceFloat64(1000, 0)
sp.NumActiveColumnsPerInhArea = 7
sp.LocalAreaDensity = -1
sp.GlobalInhibition = false
sp.inhibitionRadius = 1
trueDensity = 0.5
overlaps = utils.RandomSample(sp.numColumns)
sp.InhibitColumns(overlaps, globalFunc, localFunc)
assert.Equal(t, trueDensity, lastLocalDensity)
}
func TestInhibitColumnsGlobal(t *testing.T) {
sp := SpatialPooler{}
density := 0.3
sp.numColumns = 10
overlaps := []float64{1, 2, 1, 4, 8, 3, 12, 5, 4, 1}
active := sp.inhibitColumnsGlobal(overlaps, density)
trueActive := []int{4, 6, 7}
assert.Equal(t, trueActive, active)
density = 0.5
overlaps = []float64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}
active = sp.inhibitColumnsGlobal(overlaps, density)
trueActive = []int{5, 6, 7, 8, 9}
assert.Equal(t, trueActive, active)
}
func TestGetNeighborsND(t *testing.T) {
sp := SpatialPooler{}
dimensions := []int{5, 7, 2}
var layout [5][7][2]int
counter := 0
for i := range layout {
for j := range layout[i] {
for k := range layout[i][j] {
layout[i][j][k] = counter
counter++
}
}
}
radius := 1
x := 1
y := 3
z := 2
columnIndex := layout[z][y][x]
neighbors := sp.getNeighborsND(columnIndex, dimensions, radius, true)
var expected []int
for i := radius * -1; i <= radius; i++ {
for j := radius * -1; j <= radius; j++ {
for k := radius * -1; k <= radius; k++ {
zprime := (z + i) % dimensions[0]
yprime := (y + j) % dimensions[1]
xprime := (x + k) % dimensions[2]
if layout[zprime][yprime][xprime] != columnIndex && !utils.ContainsInt(layout[zprime][yprime][xprime], expected) {
expected = append(expected, layout[zprime][yprime][xprime])
}
}
}
}
assert.Equal(t, expected, neighbors)
dimensions = []int{5, 7, 9}
var layoutb [5][7][9]int
counter = 0
for i := range layoutb {
for j := range layoutb[i] {
for k := range layoutb[i][j] {
layoutb[i][j][k] = counter
counter++
}
}
}
radius = 3
x = 0
y = 0
z = 3
columnIndex = layoutb[z][y][x]
neighbors = sp.getNeighborsND(columnIndex, dimensions, radius, true)
expected = []int{}
for i := radius * -1; i <= radius; i++ {
for j := radius * -1; j <= radius; j++ {
for k := radius * -1; k <= radius; k++ {
zprime := utils.Mod((z + i), (dimensions[0]))
yprime := utils.Mod((y + j), (dimensions[1]))
xprime := utils.Mod((x + k), (dimensions[2]))
if layoutb[zprime][yprime][xprime] != columnIndex && !utils.ContainsInt(layoutb[zprime][yprime][xprime], expected) {
expected = append(expected, layoutb[zprime][yprime][xprime])
}
}
}
}
assert.Equal(t, expected, neighbors)
dimensions = []int{5, 10, 7, 6}
var layoutc [5][10][7][6]int
counter = 0
for i := range layoutc {
for j := range layoutc[i] {
for k := range layoutc[i][j] {
for m := range layoutc[i][j][k] {
layoutc[i][j][k][m] = counter
counter++
}
}
}
}
radius = 4
w := 2
x = 5
y = 6
z = 2
columnIndex = layoutc[z][y][x][w]
neighbors = sp.getNeighborsND(columnIndex, dimensions, radius, true)
expected = []int{}
for i := radius * -1; i <= radius; i++ {
for j := radius * -1; j <= radius; j++ {
for k := radius * -1; k <= radius; k++ {
for m := radius * -1; m <= radius; m++ {
zprime := utils.Mod((z + i), (dimensions[0]))
yprime := utils.Mod((y + j), (dimensions[1]))
xprime := utils.Mod((x + k), (dimensions[2]))
wprime := utils.Mod((w + m), (dimensions[3]))
if layoutc[zprime][yprime][xprime][wprime] != columnIndex && !utils.ContainsInt(layoutc[zprime][yprime][xprime][wprime], expected) {
expected = append(expected, layoutc[zprime][yprime][xprime][wprime])
}
}
}
}
}
assert.Equal(t, expected, neighbors)
layoutd := []bool{false, false, true, false, true, false, false, false}
columnIndex = 3
dimensions = []int{8}
radius = 1
mask := sp.getNeighborsND(columnIndex, dimensions, radius, true)
t.Log("mask", mask)
for idx, val := range layoutd {
if utils.ContainsInt(idx, mask) {
assert.Equal(t, true, val)
} else {
assert.Equal(t, false, val)
}
}
layoute := []bool{false, true, true, false, true, true, false, false}
columnIndex = 3
dimensions = []int{8}
radius = 2
mask = sp.getNeighborsND(columnIndex, dimensions, radius, true)
t.Log("mask", mask)
for idx, val := range layoute {
if utils.ContainsInt(idx, mask) {
assert.Equal(t, true, val)
} else {
assert.Equal(t, false, val)
}
}
// Wrap around
layoutf := []bool{false, true, true, false, false, false, true, true}
columnIndex = 0
dimensions = []int{8}
radius = 2
mask = sp.getNeighborsND(columnIndex, dimensions, radius, true)
t.Log("mask", mask)
for idx, val := range layoutf {
if utils.ContainsInt(idx, mask) {
assert.Equal(t, true, val)
} else {
assert.Equal(t, false, val)
}
}
//Radius too big
layoutg := []bool{true, true, true, true, true, true, false, true}
columnIndex = 6
dimensions = []int{8}
radius = 20
mask = sp.getNeighborsND(columnIndex, dimensions, radius, true)
t.Log("mask", mask)
for idx, val := range layoutg {
if utils.ContainsInt(idx, mask) {
assert.Equal(t, true, val)
} else {
assert.Equal(t, false, val)
}
}
//These are all the same tests from 2D
ints := [][]int{{0, 0, 0, 0, 0},
{0, 0, 0, 0, 0},
{0, 1, 1, 1, 0},
{0, 1, 0, 1, 0},
{0, 1, 1, 1, 0},
{0, 0, 0, 0, 0}}
layouth := NewDenseBinaryMatrixFromInts(ints)
t.Log(layouth.ToString())
columnIndex = 3*5 + 2
dimensions = []int{6, 5}
radius = 1
mask = sp.getNeighborsND(columnIndex, dimensions, radius, true)
t.Log("mask", mask)
t.Log("1d", layouth.Flatten())
for idx, val := range layouth.Flatten() {
if utils.ContainsInt(idx, mask) {
assert.Equal(t, true, val)
} else {
assert.Equal(t, false, val)
}
}
ints = [][]int{{0, 0, 0, 0, 0},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 1, 0, 1, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1}}
layoutj := NewDenseBinaryMatrixFromInts(ints)
t.Log(layouth.ToString())
columnIndex = 3*5 + 2
radius = 2
mask = sp.getNeighborsND(columnIndex, dimensions, radius, true)
t.Log("mask", mask)
t.Log("1d", layouth.Flatten())
for idx, val := range layoutj.Flatten() {
if utils.ContainsInt(idx, mask) {
assert.Equal(t, true, val)
} else {
assert.Equal(t, false, val)
}
}
//Radius too big
ints = [][]int{{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 1, 0, 1, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1}}
layoutk := NewDenseBinaryMatrixFromInts(ints)
t.Log(layouth.ToString())
columnIndex = 3*5 + 2
radius = 7
mask = sp.getNeighborsND(columnIndex, dimensions, radius, true)
t.Log("mask", mask)
t.Log("1d", layoutk.Flatten())
for idx, val := range layoutk.Flatten() {
if utils.ContainsInt(idx, mask) {
assert.Equal(t, true, val)
} else {
assert.Equal(t, false, val)
}
}
//Wrap-around
ints = [][]int{{1, 0, 0, 1, 1},
{0, 0, 0, 0, 0},
{0, 0, 0, 0, 0},
{0, 0, 0, 0, 0},
{1, 0, 0, 1, 1},
{1, 0, 0, 1, 0}}
layoutl := NewDenseBinaryMatrixFromInts(ints)
t.Log(layoutl.ToString())
columnIndex = 29
radius = 1
mask = sp.getNeighborsND(columnIndex, dimensions, radius, true)
t.Log("mask", mask)
t.Log("1d", layoutl.Flatten())
for idx, val := range layoutl.Flatten() {
if utils.ContainsInt(idx, mask) {
assert.Equal(t, true, val)
} else {
assert.Equal(t, false, val)
}
}
//No wrap around
columnIndex = 8
radius = 2
dimensions = []int{10}
neighbors = sp.getNeighborsND(columnIndex, dimensions, radius, false)
expected = []int{6, 7, 9}
assert.Equal(t, expected, neighbors)
}
func TestInhibitColumnsLocal(t *testing.T) {
sp := SpatialPooler{}
density := 0.5
sp.numColumns = 10
sp.ColumnDimensions = []int{sp.numColumns}
sp.inhibitionRadius = 2
overlaps := []float64{1, 2, 7, 0, 3, 4, 16, 1, 1.5, 1.7}
// L W W L L W W L L W
trueActive := []int{1, 2, 5, 6, 9}
active := sp.inhibitColumnsLocal(overlaps, density)
assert.Equal(t, trueActive, active)
//Test add to winners
density = 0.3333
sp.inhibitionRadius = 3
overlaps = []float64{1, 1, 1, 1, 1, 1, 1, 1, 1, 1}
// W W L L W W L L L W
trueActive = []int{0, 1, 4, 5, 8}
active = sp.inhibitColumnsLocal(overlaps, density)
assert.Equal(t, trueActive, active)
}
func TestUpdateBoostFactors(t *testing.T) {
sp := SpatialPooler{}
sp.MaxBoost = 10.0
sp.numColumns = 6
sp.minActiveDutyCycles = make([]float64, sp.numColumns)
for i := range sp.minActiveDutyCycles {
sp.minActiveDutyCycles[i] = -0.0000001
}
sp.activeDutyCycles = []float64{0.1, 0.3, 0.02, 0.04, 0.7, 0.12}
sp.boostFactors = make([]float64, sp.numColumns)
trueBoostFactors := []float64{1, 1, 1, 1, 1, 1}
sp.updateBoostFactors()
for i := range sp.boostFactors {
assert.Equal(t, trueBoostFactors[i], sp.boostFactors[i])
}
sp.MaxBoost = 10.0
sp.numColumns = 6
sp.minActiveDutyCycles = []float64{0.1, 0.3, 0.02, 0.04, 0.7, 0.12}
sp.activeDutyCycles = []float64{0.1, 0.3, 0.02, 0.04, 0.7, 0.12}
trueBoostFactors = []float64{1, 1, 1, 1, 1, 1}
sp.updateBoostFactors()
for i := range sp.boostFactors {
diff := math.Abs(trueBoostFactors[i] - sp.boostFactors[i])
assert.True(t, diff <= 0.0000001)
}
sp.MaxBoost = 10.0
sp.numColumns = 6
sp.minActiveDutyCycles = []float64{0.1, 0.2, 0.02, 0.03, 0.7, 0.12}
sp.activeDutyCycles = []float64{0.01, 0.02, 0.002, 0.003, 0.07, 0.012}
trueBoostFactors = []float64{9.1, 9.1, 9.1, 9.1, 9.1, 9.1}
sp.updateBoostFactors()
for i := range sp.boostFactors {
diff := math.Abs(trueBoostFactors[i] - sp.boostFactors[i])
assert.True(t, diff <= 0.0000001)
}
sp.MaxBoost = 10.0
sp.numColumns = 6
sp.minActiveDutyCycles = []float64{0.1, 0.2, 0.02, 0.03, 0.7, 0.12}
sp.activeDutyCycles = make([]float64, sp.numColumns)
trueBoostFactors = utils.MakeSliceFloat64(6, sp.MaxBoost)
sp.updateBoostFactors()
for i := range sp.boostFactors {
diff := math.Abs(trueBoostFactors[i] - sp.boostFactors[i])
assert.True(t, diff <= 0.0000001)
}
}
func TestUpdateDutyCycleHelper(t *testing.T) {
dc := []float64{1000.0, 1000.0, 1000.0, 1000.0, 1000.0}
period := 1000
newvals := make([]int, 5)
actual := updateDutyCyclesHelper(dc, newvals, period)
expected := []float64{999, 999, 999, 999, 999}
assert.Equal(t, expected, actual)
utils.FillSliceInt(newvals, 1000)
actual = updateDutyCyclesHelper(dc, newvals, period)
assert.Equal(t, dc, actual)
newvals = []int{2000, 4000, 5000, 6000, 7000}
expected = []float64{1001, 1003, 1004, 1005, 1006}
actual = updateDutyCyclesHelper(dc, newvals, period)
assert.Equal(t, expected, actual)
dc = []float64{1000, 800, 600, 400, 2000}
utils.FillSliceInt(newvals, 0)
period = 2
actual = updateDutyCyclesHelper(dc, newvals, period)
expected = []float64{500, 400, 300, 200, 1000}
assert.Equal(t, expected, actual)
}
func TestUpdatePermanencesForColumn(t *testing.T) {
sp := SpatialPooler{}
sp.InputDimensions = []int{5}
sp.ColumnDimensions = []int{5}
sp.numColumns = 5
sp.numInputs = 5
sp.SynPermConnected = 0.1
sp.SynPermTrimThreshold = 0.05
sp.connectedCounts = make([]int, sp.numColumns)
elms := make(map[int]float64, 25)
sp.permanences = matrix.MakeSparseMatrix(elms, sp.numColumns, sp.numInputs)
sp.potentialPools = NewDenseBinaryMatrix(sp.numColumns, sp.numInputs)
sp.connectedSynapses = NewDenseBinaryMatrix(sp.numColumns, sp.numInputs)
sp.SynPermMax = 1
sp.SynPermMin = 0
permanences := [][]float64{
{-0.10, 0.500, 0.400, 0.010, 0.020},
{0.300, 0.010, 0.020, 0.120, 0.090},
{0.070, 0.050, 1.030, 0.190, 0.060},
{0.180, 0.090, 0.110, 0.010, 0.030},
{0.200, 0.101, 0.050, -0.09, 1.100}}
/*
These are the 'true permanences' reflected in trueConnectedSynapses
truePermanences = SparseMatrix(
[[0.000, 0.500, 0.400, 0.000, 0.000],
Clip - - Trim Trim
[0.300, 0.000, 0.000, 0.120, 0.090],
- Trim Trim - -
[0.070, 0.050, 1.000, 0.190, 0.060],
- - Clip - -
[0.180, 0.090, 0.110, 0.000, 0.000],
- - - Trim Trim
[0.200, 0.101, 0.050, 0.000, 1.000]])
- - - Clip Clip
*/
trueConnectedSynapses := utils.Make2DBool([][]int{{0, 1, 1, 0, 0},
{1, 0, 0, 1, 0},
{0, 0, 1, 1, 0},